Latent Variable Model for Learning in Pairwise Markov Networks. - Saeed Amizadeh; Milos Hauskrecht (AAAI, 2010)

An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics. - Saeed Amizadeh; Shuguang Wang; Milos Hauskrecht (IJCAI, 2011)

Sampling Strategies to Evaluate the Performance of Unknown Predictors. - Hamed Valizadegan; Saeed Amizadeh; Milos Hauskrecht (SDM, 2012)

Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation. - Saeed Amizadeh; Bo Thiesson; Milos Hauskrecht (UAI, 2012)

Factorized Diffusion Map Approximation. - Saeed Amizadeh; Hamed Valizadegan; Milos Hauskrecht (AISTATS, 2012)

Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning. - Hadi Firouzi; Majid Nili Ahmadabadi; Babak Nadjar Araabi; Saeed Amizadeh; Maryam S. Mirian; Roland Siegwart (IEEE Trans. Auton. Ment. Dev., 2012)

The Bregman Variational Dual-Tree Framework. - Saeed Amizadeh; Bo Thiesson; Milos Hauskrecht (UAI, 2013)

Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series. - George D. Montañez; Saeed Amizadeh; Nikolay Laptev (AAAI, 2015)

Generic and Scalable Framework for Automated Time-series Anomaly Detection. - Nikolay Laptev; Saeed Amizadeh; Ian Flint (KDD, 2015)

Towards a Learning Optimizer for Shared Clouds. - Chenggang Wu 001; Alekh Jindal; Saeed Amizadeh; Hiren Patel; Wangchao Le; Shi Qiao 001; Sriram Rao (Proc. VLDB Endow., 2018)

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach. - Saeed Amizadeh; Sergiy Matusevych; Markus Weimer (ICLR (Poster), 2019)

Coded Elastic Computing. - Yaoqing Yang; Matteo Interlandi; Pulkit Grover; Soummya Kar; Saeed Amizadeh; Markus Weimer (ISIT, 2019)

Machine Learning at Microsoft with ML.NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Senja Filipi; Tom Finley; Abhishek Goswami; Monte Hoover; et al. (KDD, 2019)

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. - Saeed Amizadeh; Sergiy Matusevych; Markus Weimer (CoRR, 2019)

Machine Learning at Microsoft with ML .NET. - Zeeshan Ahmed; Saeed Amizadeh; Mikhail Bilenko; Rogan Carr; Wei-Sheng Chin; Yael Dekel; Xavier Dupré; Vadim Eksarevskiy; Eric Erhardt; Costin Eseanu; Senja Filipi; Tom Finley; et al. (CoRR, 2019)

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. - Gyeong-In Yu; Saeed Amizadeh; Artidoro Pagnoni; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (CoRR, 2019)

Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning". - Saeed Amizadeh; Hamid Palangi; Alex Polozov; Yichen Huang; Kazuhito Koishida (ICML, 2020)

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. - Gyeong-In Yu; Saeed Amizadeh; Sehoon Kim 001; Artidoro Pagnoni; Ce Zhang 001; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (Proc. VLDB Endow., 2021)

Weakly-supervised Audio Separation via Bi-modal Semantic Similarity. - Tanvir Mahmud; Saeed Amizadeh; Kazuhito Koishida; Diana Marculescu (ICLR, 2024)

CorrGAN: Simultaneous Learning of Speech Enhancement and Perceptual Quality Loss Functions. - Vasily Zadorozhnyy; Saeed Amizadeh; Qiang Ye 003; Kazuhito Koishida (ICASSP, 2025)

Self-reflecting Large Language Models: A Hegelian Dialectical Approach. - Sara Abdali; Can Goksen; Saeed Amizadeh; Kazuhito Koishida (CoRR, 2025)